{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Utility function for visualizing confusion matrices via matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> `from mlxtend.plotting import plot_confusion_matrix`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more information on confusion matrices, please see [`mlxtend.evaluate.confusion_matrix`](../evaluate/confusion_matrix.md)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### References\n",
    "\n",
    "- -"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 1 - Binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mlxtend.plotting import plot_confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "binary = np.array([[4, 1],\n",
    "                   [1, 2]])\n",
    "\n",
    "fig, ax = plot_confusion_matrix(conf_mat=binary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 2 - Binary absolute and relative with colorbar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "binary = np.array([[4, 1],\n",
    "                   [1, 2]])\n",
    "\n",
    "fig, ax = plot_confusion_matrix(conf_mat=binary,\n",
    "                                show_absolute=True,\n",
    "                                show_normed=True,\n",
    "                                colorbar=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example 3 - Multiclass relative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "multiclass = np.array([[2, 1, 0, 0],\n",
    "                       [1, 2, 0, 0],\n",
    "                       [0, 0, 1, 0],\n",
    "                       [0, 0, 0, 1]])\n",
    "\n",
    "fig, ax = plot_confusion_matrix(conf_mat=multiclass,\n",
    "                                colorbar=True,\n",
    "                                show_absolute=False,\n",
    "                                show_normed=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "## plot_confusion_matrix\n",
      "\n",
      "*plot_confusion_matrix(conf_mat, hide_spines=False, hide_ticks=False, figsize=None, cmap=None, colorbar=False, show_absolute=True, show_normed=False)*\n",
      "\n",
      "Plot a confusion matrix via matplotlib.\n",
      "**Parameters**\n",
      "\n",
      "- `conf_mat` : array-like, shape = [n_classes, n_classes]\n",
      "\n",
      "    Confusion matrix from evaluate.confusion matrix.\n",
      "\n",
      "- `hide_spines` : bool (default: False)\n",
      "\n",
      "    Hides axis spines if True.\n",
      "\n",
      "- `hide_ticks` : bool (default: False)\n",
      "\n",
      "    Hides axis ticks if True\n",
      "\n",
      "- `figsize` : tuple (default: (2.5, 2.5))\n",
      "\n",
      "    Height and width of the figure\n",
      "\n",
      "- `cmap` : matplotlib colormap (default: `None`)\n",
      "\n",
      "    Uses matplotlib.pyplot.cm.Blues if `None`\n",
      "\n",
      "- `colorbar` : bool (default: False)\n",
      "\n",
      "    Shows a colorbar if True\n",
      "\n",
      "- `show_absolute` : bool (default: True)\n",
      "\n",
      "    Shows absolute confusion matrix coefficients if True.\n",
      "    At least one of  `show_absolute` or `show_normed`\n",
      "    must be True.\n",
      "\n",
      "- `show_normed` : bool (default: False)\n",
      "\n",
      "    Shows normed confusion matrix coefficients if True.\n",
      "    The normed confusion matrix coefficients give the\n",
      "    proportion of training examples per class that are\n",
      "    assigned the correct label.\n",
      "    At least one of  `show_absolute` or `show_normed`\n",
      "    must be True.\n",
      "**Returns**\n",
      "\n",
      "- `fig, ax` : matplotlib.pyplot subplot objects\n",
      "\n",
      "    Figure and axis elements of the subplot.\n",
      "**Examples**\n",
      "\n",
      "For usage examples, please see\n",
      "    [http://rasbt.github.io/mlxtend/user_guide/plotting/plot_confusion_matrix/](http://rasbt.github.io/mlxtend/user_guide/plotting/plot_confusion_matrix/)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('../../api_modules/mlxtend.plotting/plot_confusion_matrix.md', 'r') as f:\n",
    "    s = f.read() \n",
    "print(s)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
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